Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

TIMESCAPES: an architectural process of memory and adaptation

What if the architectural process of making could incorporate time? All designers who impact the physical environment- consciously and unconsciously are gatekeepers of the past, commentators of the present, and speculators of the future. This project proposes the creation of architecture and adaptive public space that looks to historical memories, foster present day cultural formation, and new alternative visions for the city of the future. The thesis asks what it means to design for stasis and change in a variety of scales- urban, architectural, and detail and arrives at a speculated new neighborhood, institutional buildings, and landscape. Central to this project is the idea of the architect as archeologist, anthropologist, and artist. The project focuses on a rapidly changing part of the city of Fort Worth, Texas and assigns a multipurpose institutional buildings and public space as a method of investigation. The thesis hopes to further architectural discourse about into the role of architecture in the preservation of memory, adaptive potential of public spaces, and the role of time in architecture.

The Indistinct Edge: Reconnecting Experience in Nature and Architecture

This thesis explores how architectures sense of place is rooted in the natural environment. The built environment has been constructed to protect and sustain human culture from the weathering of nature. Separating experience from the natural environment removes a sense of place and belonging in the natural and reinforces architectural dominance. This separation distinguishes the natural world as an article of spectacle and gives the human experience an unnatural voyeurship to natural changes. By examining the fusion of architectural and natural edges this thesis analyzes how the human experience can reconnect with a naturalistic sense of place through architecture, blending the finite edge where architecture maintains nature, and adapting buildings to the cycles of the environment. Removing dominance of man-made spaces and replacing them with the cohabitation of the edge between built and natural forms.

Place in Time: The Role of Architecture in Establishing an Emotional Connection between Man and Time

This thesis explores the role of architecture as a means of reconnecting humans to the passage of time. A neglect of the temporal in our built environment obscures understanding of the human condition in all of its sensory aspects. The exploration and design of a series of ritual engagements, both culturally, and architecturally, begin to offer a venue through which designers can engage human senses. Rituals act as a means of demarcating the passage of time. It is through the engagement with these moments that people can begin to gain a richer understanding of the ephemeral nature of their own existence. The Pritzker Architecture Prize serves as the selected ritual of exploration because of its celebration of humanity and the art of architecture. However, the notion of ritual is explored down to the level of detail of engagement with handrails and door handles.